Google - Amazon Workplace Reviews

1 Load the required packages

library(tm)

## Loading required package: NLP

library(dplyr)

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library(SnowballC)
library(RCurl)

## Loading required package: bitops

library(qdap)

## Loading required package: qdapDictionaries

## Loading required package: qdapRegex

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## Loading required package: qdapTools

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library(wordcloud)
library(RWeka)
library(plotly)

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2 Load the dataset

amazon_url<- getURL('http://s3.amazonaws.com/assets.datacamp.com/production/course_935/datasets/500_amzn.csv',
                    ssl.verifyhost=FALSE, ssl.verifypeer=FALSE)
amazon <- read.csv(textConnection(amazon_url), header = TRUE)



google_url<- getURL('http://s3.amazonaws.com/assets.datacamp.com/production/course_935/datasets/500_goog.csv',
                    ssl.verifyhost=FALSE, ssl.verifypeer=FALSE)
google<- read.csv(textConnection(google_url), header = TRUE)

Amazon Pros

amzn_pros <- amazon$pros
amzn_pros <- amzn_pros[!is.na(amzn_pros)]

Amazon Cons

amzn_cons <- amazon$cons
amzn_cons <- amzn_cons[!is.na(amzn_cons)]

Google Pros

goog_pros <- google$pros
goog_pros <- goog_pros[!is.na(goog_pros)]

Google Cons

goog_cons <- google$cons
goog_cons <- goog_cons[!is.na(goog_cons)]

3 Create the cleaning and Word Cloud Functions

#STEP 1: qdap_clean function

qdap_clean <- function(x) {
  x <- replace_number(x)
  x <- replace_symbol(x)
  x <- replace_abbreviation(x)
  x <- replace_contraction(x)
  x <- replace_ordinal(x)
  x <- tolower(x)
  return(x)
}


# STEP 2: Corpus cleaning function using "tm"


tm_clean <- function(corpus) {
  corpus<- tm_map(corpus, removePunctuation)
  corpus <- tm_map(corpus, stripWhitespace)
  corpus <- tm_map(corpus, removeWords,
                   c(stopwords("en"), "Google","google","amazon", "Amazon","company"))
  return(corpus)
}


create_wordcloud = function(corpus, color) 
{
  pal = brewer.pal(n = 7, name = color)
  tokenizer = function(x) {NGramTokenizer(x, Weka_control(min = 2, max = 2))}
  corpus_tdm = TermDocumentMatrix(corpus, control = list(tokenize = tokenizer))
  corpus_tdm = removeSparseTerms(x = corpus_tdm, sparse = 0.99)
  corpus_tdm = as.data.frame(as.matrix(corpus_tdm))
  corpus_tdm_freq = rowSums(corpus_tdm)
  return(wordcloud(names(corpus_tdm_freq), freq = corpus_tdm_freq, scale = c(3.0, 1.0), colors = pal, min.freq = 3) )
}

4 Create Corpus

amzn_pros_corp <- tm_clean(VCorpus(VectorSource(qdap_clean(amzn_pros))))

amzn_cons_corp <- tm_clean(VCorpus(VectorSource(qdap_clean(amzn_cons))))

goog_pros_corp <- tm_clean(VCorpus(VectorSource(qdap_clean(goog_pros))))

goog_cons_corp <- tm_clean(VCorpus(VectorSource(qdap_clean(goog_cons))))

5 Create Wordclouds

5a Amazon Pros Wordcloud

create_wordcloud(corpus = amzn_pros_corp, color = "Reds")

## Warning in wordcloud(names(corpus_tdm_freq), freq = corpus_tdm_freq, scale
## = c(3, : fast paced could not be fit on page. It will not be plotted.

## Warning in wordcloud(names(corpus_tdm_freq), freq = corpus_tdm_freq, scale
## = c(3, : good pay could not be fit on page. It will not be plotted.

## Warning in wordcloud(names(corpus_tdm_freq), freq = corpus_tdm_freq, scale
## = c(3, : smart people could not be fit on page. It will not be plotted.

## Warning in wordcloud(names(corpus_tdm_freq), freq = corpus_tdm_freq, scale
## = c(3, : great pay could not be fit on page. It will not be plotted.

5b Amazon Cons Wordcloud

create_wordcloud(corpus = amzn_cons_corp, color = "Reds")

5c Google Pros Wordcloud

create_wordcloud(corpus = goog_pros_corp, color = "Reds")

## Warning in wordcloud(names(corpus_tdm_freq), freq = corpus_tdm_freq, scale
## = c(3, : smart people could not be fit on page. It will not be plotted.

5d Google Cons Wordcloud

create_wordcloud(corpus = goog_cons_corp, color = "Reds")

6 Create Comparison Cloud

6a Clean the text using qdap function

amzn_pros_cc <- qdap_clean(amzn_pros)
amzn_cons_cc <- qdap_clean(amzn_cons)
goog_pros_cc <- qdap_clean(goog_pros)
goog_cons_cc <- qdap_clean(goog_cons)

6b Merge the pros and cons for comparison

amznproscc <- paste(amzn_pros_cc, collapse = "")
amznconscc <- paste(amzn_cons_cc, collapse="")
googproscc <- paste(goog_pros_cc, collapse= "")
googconscc <- paste(goog_cons_cc, collapse= "")
amzn_all <- c(amznproscc, amznconscc)
goog_all <- c(googproscc, googconscc)

6c. Clean the Corpus, Create TDM and create matrix for comparison cloud

amzn_all_clean <- tm_clean(VCorpus(VectorSource(amzn_all)))
goog_all_clean <- tm_clean(VCorpus(VectorSource(goog_all)))

amzn_all_tdm <- TermDocumentMatrix(amzn_all_clean)
goog_all_tdm <- TermDocumentMatrix(goog_all_clean)

colnames(amzn_all_tdm) <- c("Amzn_Pros", "Amzn_Cons")
colnames(goog_all_tdm) <- c("Goog_Pros", "Goog_Cons")
amzn_all_m <- as.matrix(amzn_all_tdm)
goog_all_m <- as.matrix(goog_all_tdm)

6d Comparison Cloud for Amazon

comparison.cloud(amzn_all_m, colors = c("Red", "Green"), max.words = 100)

6e Comparison Cloud for Google

comparison.cloud(goog_all_m, colors = c("Red", "Green"), max.words = 100)

## Warning in comparison.cloud(goog_all_m, colors = c("Red", "Green"),
## max.words = 100): compensation could not be fit on page. It will not be
## plotted.

## Warning in comparison.cloud(goog_all_m, colors = c("Red", "Green"),
## max.words = 100): employees could not be fit on page. It will not be
## plotted.

7 Common Bigram Word Analysis

7a Prepare the data

amzn_pros_df <- data.frame(text=unlist(sapply(amzn_pros_corp, '[',"content")),stringsAsFactors=F)
token_delim <- " \\t\\r\\n.!?,;\"()"
bi_amzn_pros <- NGramTokenizer(amzn_pros_df, Weka_control(min=2,max=2, delimiters = token_delim))
bi_amzn_pros_df <- data.frame(table(bi_amzn_pros))


amzn_cons_df <- data.frame(text=unlist(sapply(amzn_cons_corp, '[',"content")),stringsAsFactors=F)
token_delim <- " \\t\\r\\n.!?,;\"()"
bi_amzn_cons <- NGramTokenizer(amzn_cons_df, Weka_control(min=2,max=2, delimiters = token_delim))
bi_amzn_cons_df <- data.frame(table(bi_amzn_cons))

goog_pros_df <- data.frame(text=unlist(sapply(goog_pros_corp, '[',"content")),stringsAsFactors=F)
token_delim <- " \\t\\r\\n.!?,;\"()"
bi_goog_pros <- NGramTokenizer(goog_pros_df, Weka_control(min=2,max=2))
bi_goog_pros_df <- data.frame(table(bi_goog_pros))


goog_cons_df <- data.frame(text=unlist(sapply(goog_cons_corp, '[',"content")),stringsAsFactors=F)
token_delim <- " \\t\\r\\n.!?,;\"()"
bi_goog_cons <- NGramTokenizer(goog_cons_df, Weka_control(min=2,max=2, delimiters = token_delim))
bi_goog_cons_df <- data.frame(table(bi_goog_cons))

7b Top 5 common bigram words in Pros reviews

all_pros_df <- left_join(bi_amzn_pros_df,bi_goog_pros_df, by=c("bi_amzn_pros" = "bi_goog_pros"),stringsAsFactors=F)

## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector

colnames(all_pros_df) <-c("Term","Amazon Pros" ,"Google Pros") 
all_pros_df <- na.omit(all_pros_df)
all_pros_df$Total <-(all_pros_df[, 2] +all_pros_df[, 3])
all_pros_total <- all_pros_df %>% arrange(desc(Total)) %>% head(5)
plot_ly(all_pros_total, x = ~all_pros_total$Term, y = ~all_pros_total$`Amazon Pros`, type = 'bar', name = 'Amazon Pros') %>%
  add_trace(y = ~all_pros_total$`Google Pros`, name = 'Google Pros') %>%
  layout(yaxis = list(title = 'Count'),xaxis = list(title = 'Top 5 common bigram words in Pros reviews '), barmode = 'stack')

7c Top 5 common bigram words in Cons reviews

all_cons_df <-left_join(bi_amzn_cons_df,bi_goog_cons_df, by=c("bi_amzn_cons" = "bi_goog_cons"),stringsAsFactors=F) #to find out the common words between two companies

## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factors with different levels, coercing to character vector

colnames(all_cons_df) <-c("Term","Amazon Cons" ,"Google Cons") 
all_cons_df <- na.omit(all_cons_df) # 
all_cons_df$Total <-(all_cons_df[, 2] +all_cons_df[, 3])
all_cons_total <- all_cons_df %>% arrange(desc(Total)) %>% head(5)

plot_ly(all_cons_total, x = ~all_cons_total$Term, y = ~all_cons_total$`Amazon Cons`, type = 'bar', name = 'Amazon Cons') %>%
  add_trace(y = ~all_cons_total$`Google Cons`, name = 'Google Cons') %>%
  layout(yaxis = list(title = 'Count'),xaxis = list(title = 'Top 5 common bigram words in Cons reviews'), barmode = 'stack')